Required packages for analysis

#install.packages("covid19.analytics")

#install.packages("devtools")
devtools::install_github("mponce0/covid19.analytics")
## Downloading GitHub repo mponce0/covid19.analytics@master
library(covid19.analytics)

To obtain all the records combined for “confirmed”, “deaths” and “recovered” cases – aggregated data

covid19.data.ALLcases <- covid19.data()

To obtain time series data for “confirmed” cases

covid19.confirmed.cases <- covid19.data("ts-confirmed")

Reads all possible datasets, returning a list

covid19.all.datasets <- covid19.data("ALL")

Reads the latest aggregated data

covid19.ALL.agg.cases <- covid19.data("aggregated")

Reads time series data for casualties

covid19.TS.deaths <- covid19.data("ts-deaths")

To obtain covid19’s genomic data

covid19.gen.seq <- covid19.genomic.data()

To display the actual RNA seq

covid19.gen.seq$NC_045512.2

report.summary()
## Data being read from JHU/CCSE repository
## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv
## Data retrieved on 2020-08-31 20:32:08 || Range of dates on data: 2020-01-22--2020-08-30 | Nbr of records: 266
##  >>> checking data integrity...
## checking for ... Country Province Lat Long
## No critical issues have been found.
##  >>> checking data consistency...
## Data being read from JHU/CCSE repository
## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv
## Data retrieved on 2020-08-31 20:32:09 || Range of dates on data: 2020-01-22--2020-08-30 | Nbr of records: 266
##  >>> checking data integrity...
## checking for ... Country Province Lat Long
## No critical issues have been found.
##  >>> checking data consistency...

## Data being read from JHU/CCSE repository
## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv
## Data retrieved on 2020-08-31 20:32:10 || Range of dates on data: 2020-01-22--2020-08-30 | Nbr of records: 253
##  >>> checking data integrity...
## checking for ... Country Province Lat Long
## No critical issues have been found.
##  >>> checking data consistency...

## Data being read from JHU/CCSE repository
## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/08-30-2020.csv
##  >>> checking data integrity...
## checking for ... Country Province Lat Long
## No critical issues have been found.
## Possible <<Aggregated data-type>> detected...
## checking for ... Active Deaths Recovered Confirmed
## No critical issues have been found.
## *** 14 entries were removed due to data inconsistences
##  >>> checking data consistency...
## This function applies to TimeSeries data only

##  >>> checking data integrity...
## checking for ... Country Province Lat Long
## No critical issues have been found.
## Possible <<Aggregated data-type>> detected...
## checking for ... Active Deaths Recovered Confirmed
## No critical issues have been found.

Save the tables into a text file named ‘covid19-SummaryReport_CURRENTDATE.txt’ where CURRRENTDATE is the actual date

report.summary(saveReport=TRUE)
## Data being read from JHU/CCSE repository
## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv
## Data retrieved on 2020-08-31 20:32:13 || Range of dates on data: 2020-01-22--2020-08-30 | Nbr of records: 266
## --------------------------------------------------------------------------------
##  >>> checking data integrity...
## checking for ... Country Province Lat Long
## No critical issues have been found.
##  >>> checking data consistency...
## Warning in consistency.check(data, n0, nf, datasetName, details = details, :
## Inconsistency of type.II in ts-confirmed data detected -- 47 records (out of
## 266) show inconsistencies in the data...
## ################################################################################ 
##   ##### TS-CONFIRMED Cases  -- Data dated:  2020-08-30  ::  2020-08-31 20:32:13 
## ################################################################################ 
##   Number of Countries/Regions reported:  188 
##   Number of Cities/Provinces reported:  82 
##   Unique number of distinct geographical locations combined: 266 
## -------------------------------------------------------------------------------- 
##   Worldwide ts-confirmed  Totals: 25222709 
## -------------------------------------------------------------------------------- 
##    Country.Region Province.State  Totals GlobalPerc LastDayChange   t-2   t-3   t-7  t-14  t-30
## 1              US                5996431      23.77         35337 47153 46156 37891 35112 58485
## 2          Brazil                3862311      15.31         16158 41350 43412 17078 19373 45392
## 3           India                3621245      14.36         78512 78761 76472 60975 55018 54735
## 4          Russia                 987470       3.92          4897  4843  4758  4688  4839  5429
## 5            Peru                 639435       2.54          9474  7964  8619  9090 10143     0
## 6    South Africa                 625056       2.48          2505  2419  1846  1677  2541 10107
## 7        Colombia                 607904       2.41          8020  9392  8497 10549  8328 10673
## 8          Mexico                 595841       2.36          4129  5974  5824  3541  3571  9556
## 9           Spain                 439286       1.74             0     0  9779 19382 16269     0
## 10          Chile                 409974       1.63          1965  2037  1870  1903  1556  1991
## -------------------------------------------------------------------------------- 
##   Global Perc. Average:  0.38 (sd: 1.98) 
##   Global Perc. Average in top  10 :  7.05 (sd: 7.84) 
## -------------------------------------------------------------------------------- 
## ================================================================================
## Data being read from JHU/CCSE repository
## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv
## Data retrieved on 2020-08-31 20:32:14 || Range of dates on data: 2020-01-22--2020-08-30 | Nbr of records: 266
## --------------------------------------------------------------------------------
##  >>> checking data integrity...
## checking for ... Country Province Lat Long
## No critical issues have been found.
##  >>> checking data consistency...
## Warning in consistency.check(data, n0, nf, datasetName, details = details, :
## Inconsistency of type.II in ts-deaths data detected -- 35 records (out of 266)
## show inconsistencies in the data...

## ################################################################################ 
##   ##### TS-DEATHS Cases  -- Data dated:  2020-08-30  ::  2020-08-31 20:32:15 
## ################################################################################ 
##   Number of Countries/Regions reported:  188 
##   Number of Cities/Provinces reported:  82 
##   Unique number of distinct geographical locations combined: 266 
## -------------------------------------------------------------------------------- 
##   Worldwide ts-deaths  Totals: 846395 
## -------------------------------------------------------------------------------- 
##    Country.Region Province.State Totals  Perc LastDayChange t-2  t-3 t-7 t-14 t-30
## 1              US                183066  3.05           305 961  976 445  445 1111
## 2          Brazil                120828  3.13           566 758  855 565  684 1088
## 3           India                 64469  1.78           971 948 1021 848  876  853
## 4          Mexico                 64158 10.77           339 673  552 320  266  784
## 5  United Kingdom                 41499 12.41             1  12    9   4    3   13
## 6           Italy                 35477 13.23             4   1    9   4    4    5
## 7          France                 30470 10.18            10   0   19  14   23    0
## 8           Spain                 29011  6.60             0   0   15  34   29    0
## 9            Peru                 28607  4.47           136 194  153 210  206    0
## 10           Iran                 21462  5.75           103 110  112 133  165  216
## -------------------------------------------------------------------------------- 
## -------------------------------------------------------------------------------- 
## ================================================================================
## Data being read from JHU/CCSE repository
## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv
## Data retrieved on 2020-08-31 20:32:15 || Range of dates on data: 2020-01-22--2020-08-30 | Nbr of records: 253
## --------------------------------------------------------------------------------
##  >>> checking data integrity...
## checking for ... Country Province Lat Long
## No critical issues have been found.
##  >>> checking data consistency...
## Warning in consistency.check(data, n0, nf, datasetName, details = details, :
## Inconsistency of type.II in ts-recovered data detected -- 69 records (out of
## 253) show inconsistencies in the data...

## ################################################################################ 
##   ##### TS-RECOVERED Cases  -- Data dated:  2020-08-30  ::  2020-08-31 20:32:16 
## ################################################################################ 
##   Number of Countries/Regions reported:  188 
##   Number of Cities/Provinces reported:  68 
##   Unique number of distinct geographical locations combined: 253 
## -------------------------------------------------------------------------------- 
##   Worldwide ts-recovered  Totals: 16618168 
## -------------------------------------------------------------------------------- 
##    Country.Region Province.State  Totals LastDayChange   t-2   t-3   t-7  t-14  t-30
## 1          Brazil                3237615         35430 43402 35937 28472 44063 29128
## 2           India                2774801         60868 64935 65050 66550 57829 51255
## 3              US                2153939         13325 22247 17041 23013 32513 23725
## 4          Russia                 804941          2576  5867  5869  2451  3129  8099
## 5    South Africa                 538604          1910  2759  2597 10024  5294 16290
## 6          Mexico                 489724          5441  4513  4238  8086  6542  7752
## 7        Colombia                 450609         10047 10954 11827 10141 14089  6321
## 8            Peru                 446675          8658  8355  7785     0  5350  3212
## 9           Chile                 382584          1401  1731  1530  1285  1557  2180
## 10           Iran                 321421          1574  1577  1632  1901  1671  2311
## -------------------------------------------------------------------------------- 
## -------------------------------------------------------------------------------- 
## ================================================================================
## Data being read from JHU/CCSE repository
## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## Reading data from https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/08-30-2020.csv
##  >>> checking data integrity...
## checking for ... Country Province Lat Long
## No critical issues have been found.
## Possible <<Aggregated data-type>> detected...
## checking for ... Active Deaths Recovered Confirmed
## No critical issues have been found.
## Warning in chck.cols.qty(agg.critical.cols, data, disclose = disclose): Column Active has 4 entries reporting negative values!
##   on entries: 471 1426 1871 3068
## Warning in chck.cols.qty(agg.critical.cols, data, disclose = disclose): number of 'active+recovered+deaths' cases does NOT match the number of 'confirmed' cases!
##  on 10 entries -- 164 199 324 362 405 1313 1588 1846 2777 2877
##   ||  FIPS  Admin2  Province_State  Country_Region  Last_Update  Lat  Long_  Confirmed  Deaths  Recovered  Active  Combined_Key  Incidence_Rate  Case.Fatality_Ratio
##   164 199 324 362 405 1313 1588 1846 2777 2877 || c(NA, NA, NA, NA, NA, 80017, 20099, 90023, 39125, 90040)  c("", "", "", "", "", "Out of IL", "Labette", "Unassigned", "Paulding", "Unassigned")  c("Vichada", "Saint Pierre and Miquelon", "Nagasaki", "", "", "Illinois", "Kansas", "Maine", "Ohio", "Oklahoma")  c("Colombia", "France", "Japan", "Luxembourg", "Monaco", "US", "US", "US", "US", "US")  c("2020-08-31 04:28:27", "2020-08-31 04:28:27", "2020-08-31 04:28:27", "2020-08-31 04:28:27", "2020-08-31 04:28:27", "2020-08-31 04:28:27", "2020-08-31 04:28:27", "2020-08-31 04:28:27", "2020-08-31 04:28:27", "2020-08-31 04:28:27")  c(4.4234, 46.8852, 33.235712, 49.8153, 43.7333, NA, 37.19113093, NA, 41.11676341, NA)  c(-69.2878, -56.3159, 129.608033, 6.1296, 7.4167, NA, -95.29849679, NA, -84.5801017, NA)  c(21, 5, 229, 6625, 154, 2, 183, 3, 79, 103)  c(1, 0, 3, 124, 4, 0, 1, 0, 1, 0)  c(5, 1, 36, 7140, 116, 0, 0, 0, 0, 0)  c(36, 1, 47, 6549, 41, 0, 180, 1, 81, 4)  c("Vichada, Colombia", "Saint Pierre and Miquelon, France", "Nagasaki, Japan", "Luxembourg", "Monaco", "Out of IL, Illinois, US", "Labette, Kansas, US", "Unassigned, Maine, US", "Paulding, Ohio, US", "Unassigned, Oklahoma, US")  c(51.0166221430691, 86.2812769628991, 17.3385479644545, 1066.01531049114, 333.808989909286, NA, 932.816800897135, NA, 433.804627249357, NA)  c(4.76190476190476, 0, 1.31004366812227, 1.87169811320755, 2.5974025974026, 0, 0.546448087431694, 0, 1.26582278481013, 0)
## *** 14 entries were removed due to data inconsistences
##  >>> checking data consistency...
## This function applies to TimeSeries data only

## ############################################################################################################################################ 
##   ##### AGGREGATED Data  -- ORDERED BY  CONFIRMED Cases  -- Data dated:  2020-08-31  ::  2020-08-31 20:32:16 
## ############################################################################################################################################ 
##   Number of Countries/Regions reported: 186 
##   Number of Cities/Provinces reported: 559 
##   Unique number of distinct geographical locations combined: 3936 
## -------------------------------------------------------------------------------------------------------------------------------------------- 
##                 Location Confirmed Perc.Confirmed Deaths Perc.Deaths Recovered Perc.Recovered Active Perc.Active
## 1      Sao Paulo, Brazil    803404           3.19  29978        3.73    625203          77.82 148223       18.45
## 2     Maharashtra, India    780689           3.10  24399        3.13    562401          72.04 193889       24.84
## 3           South Africa    625056           2.48  14028        2.24    538604          86.17  72424       11.59
## 4  Andhra Pradesh, India    424767           1.68   3884        0.91    321754          75.75  99129       23.34
## 5      Tamil Nadu, India    422085           1.67   7231        1.71    362133          85.80  52721       12.49
## 6              Argentina    408426           1.62   8457        2.07    294007          71.99 105962       25.94
## 7                   Iran    373570           1.48  21462        5.75    321421          86.04  30687        8.21
## 8       Karnataka, India    335928           1.33   5589        1.66    242229          72.11  88110       26.23
## 9           Saudi Arabia    314821           1.25   3870        1.23    289667          92.01  21284        6.76
## 10            Bangladesh    310822           1.23   4248        1.37    201907          64.96 104667       33.67
## ============================================================================================================================================
## ############################################################################################################################################ 
##   ##### AGGREGATED Data  -- ORDERED BY  DEATHS Cases  -- Data dated:  2020-08-31  ::  2020-08-31 20:32:16 
## ############################################################################################################################################ 
##   Number of Countries/Regions reported: 186 
##   Number of Cities/Provinces reported: 559 
##   Unique number of distinct geographical locations combined: 3936 
## -------------------------------------------------------------------------------------------------------------------------------------------- 
##                       Location Confirmed Perc.Confirmed Deaths Perc.Deaths Recovered Perc.Recovered Active Perc.Active
## 1      England, United Kingdom    288989           1.15  36850       12.75         0           0.00 252139       87.25
## 2                       France    299320           1.19  30470       10.18     73279          24.48 195571       65.34
## 3            Sao Paulo, Brazil    803404           3.19  29978        3.73    625203          77.82 148223       18.45
## 4           Maharashtra, India    780689           3.10  24399        3.13    562401          72.04 193889       24.84
## 5  New York City, New York, US    233969           0.93  23689       10.12         0           0.00 210280       89.88
## 6                         Iran    373570           1.48  21462        5.75    321421          86.04  30687        8.21
## 7             Lombardia, Italy     99940           0.40  16863       16.87     76248          76.29   6829        6.83
## 8       Rio de Janeiro, Brazil    223302           0.89  16027        7.18    201715          90.33   5560        2.49
## 9                 South Africa    625056           2.48  14028        2.24    538604          86.17  72424       11.59
## 10                  Lima, Peru    304567           1.21  12668        4.16         0           0.00 291899       95.84
## ============================================================================================================================================
## ############################################################################################################################################ 
##   ##### AGGREGATED Data  -- ORDERED BY  RECOVERED Cases  -- Data dated:  2020-08-31  ::  2020-08-31 20:32:16 
## ############################################################################################################################################ 
##   Number of Countries/Regions reported: 186 
##   Number of Cities/Provinces reported: 559 
##   Unique number of distinct geographical locations combined: 3936 
## -------------------------------------------------------------------------------------------------------------------------------------------- 
##                 Location Confirmed Perc.Confirmed Deaths Perc.Deaths Recovered Perc.Recovered Active Perc.Active
## 1      Sao Paulo, Brazil    803404           3.19  29978        3.73    625203          77.82 148223       18.45
## 2     Maharashtra, India    780689           3.10  24399        3.13    562401          72.04 193889       24.84
## 3           South Africa    625056           2.48  14028        2.24    538604          86.17  72424       11.59
## 4      Tamil Nadu, India    422085           1.67   7231        1.71    362133          85.80  52721       12.49
## 5  Andhra Pradesh, India    424767           1.68   3884        0.91    321754          75.75  99129       23.34
## 6                   Iran    373570           1.48  21462        5.75    321421          86.04  30687        8.21
## 7              Argentina    408426           1.62   8457        2.07    294007          71.99 105962       25.94
## 8           Saudi Arabia    314821           1.25   3870        1.23    289667          92.01  21284        6.76
## 9   Metropolitana, Chile    273239           1.08   8517        3.12    259943          95.13   4779        1.75
## 10                Turkey    268546           1.07   6326        2.36    243839          90.80  18381        6.84
## ============================================================================================================================================
## ############################################################################################################################################ 
##   ##### AGGREGATED Data  -- ORDERED BY  ACTIVE Cases  -- Data dated:  2020-08-31  ::  2020-08-31 20:32:17 
## ############################################################################################################################################ 
##   Number of Countries/Regions reported: 186 
##   Number of Cities/Provinces reported: 559 
##   Unique number of distinct geographical locations combined: 3936 
## -------------------------------------------------------------------------------------------------------------------------------------------- 
##                       Location Confirmed Perc.Confirmed Deaths Perc.Deaths Recovered Perc.Recovered Active Perc.Active
## 1                   Lima, Peru    304567           1.21  12668        4.16         0           0.00 291899       95.84
## 2      England, United Kingdom    288989           1.15  36850       12.75         0           0.00 252139       87.25
## 3  Los Angeles, California, US    240749           0.95   5769        2.40         0           0.00 234980       97.60
## 4  New York City, New York, US    233969           0.93  23689       10.12         0           0.00 210280       89.88
## 5                       France    299320           1.19  30470       10.18     73279          24.48 195571       65.34
## 6           Maharashtra, India    780689           3.10  24399        3.13    562401          72.04 193889       24.84
## 7      Miami-Dade, Florida, US    156559           0.62   2403        1.53         0           0.00 154156       98.47
## 8            Sao Paulo, Brazil    803404           3.19  29978        3.73    625203          77.82 148223       18.45
## 9        Maricopa, Arizona, US    133641           0.53   2962        2.22         0           0.00 130679       97.78
## 10          Cook, Illinois, US    126003           0.50   5054        4.01         0           0.00 120949       95.99
## ============================================================================================================================================
##       Confirmed  Deaths  Recovered   Active 
##   Totals 
##       25215305   846034  14010256    NA 
##   Average 
##       6406.33    214.95  3559.52 NA 
##   Standard Deviation 
##       33365.51   1374.85 25270.64    NA 
##   
## 
##  * Statistical estimators computed considering 3936 independent reported entries
##  >>> checking data integrity...
## checking for ... Country Province Lat Long
## No critical issues have been found.
## Possible <<Aggregated data-type>> detected...
## checking for ... Active Deaths Recovered Confirmed
## No critical issues have been found.
##  
## 
## ******************************************************************************** 
## ********************************  OVERALL SUMMARY******************************** 
## ******************************************************************************** 
##   ****  Time Series Worldwide TOTS **** 
##       ts-confirmed   ts-deaths   ts-recovered 
##       25222709   846395  16618168 
##              3.36%       65.89% 
##   ****  Time Series Worldwide AVGS **** 
##       ts-confirmed   ts-deaths   ts-recovered 
##       94822.21   3181.94 65684.46 
##              3.36%       69.27% 
##   ****  Time Series Worldwide SDS **** 
##       ts-confirmed   ts-deaths   ts-recovered 
##       498368.72  15234.1 310137.5 
##              3.06%       62.23% 
##   
## 
##  * Statistical estimators computed considering 266/266/253 independent reported entries per case-type 
## ********************************************************************************
## Report saved in covid19-SummaryReport_2020-08-31.txt

Totals for confirmed cases for “Germany, India”

tots.per.location(covid19.confirmed.cases,geo.loc="Germany")
## [1] "GERMANY"
## GERMANY  --  243305 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -49804 -25831  -3583  26784  48555 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -13492.23    3983.62  -3.387 0.000837 ***
## x.var         1279.46      30.98  41.305  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 29580 on 220 degrees of freedom
## Multiple R-squared:  0.8858, Adjusted R-squared:  0.8853 
## F-statistic:  1706 on 1 and 220 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.8443 -2.0526  0.0925  2.1057  3.3548 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.607112   0.307980   14.96   <2e-16 ***
## x.var       0.047447   0.002395   19.81   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.287 on 220 degrees of freedom
## Multiple R-squared:  0.6408, Adjusted R-squared:  0.6392 
## F-statistic: 392.6 on 1 and 220 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -307.50  -241.43   -21.13   141.07   215.97  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.033e+01  5.399e-04   19138   <2e-16 ***
## x.var       1.082e-02  3.329e-06    3251   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 19657517  on 221  degrees of freedom
## Residual deviance:  7592929  on 220  degrees of freedom
## AIC: 7595526
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------

tots.per.location(covid19.confirmed.cases,geo.loc="India", confBnd=TRUE)
## [1] "INDIA"
## INDIA  --  3621245 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -620147 -490819 -121787  369042 1791459 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -702712.4    75814.2  -9.269   <2e-16 ***
## x.var         11407.7      589.5  19.351   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 562900 on 220 degrees of freedom
## Multiple R-squared:  0.6299, Adjusted R-squared:  0.6282 
## F-statistic: 374.5 on 1 and 220 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.69811 -1.21814 -0.07388  1.40734  2.07597 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 1.132410   0.194270   5.829 1.97e-08 ***
## x.var       0.073800   0.001511  48.855  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.442 on 220 degrees of freedom
## Multiple R-squared:  0.9156, Adjusted R-squared:  0.9152 
## F-statistic:  2387 on 1 and 220 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -343.81  -133.15   -74.02    62.66   138.11  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 7.568e+00  6.134e-04   12337   <2e-16 ***
## x.var       3.473e-02  3.132e-06   11090   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 267563605  on 221  degrees of freedom
## Residual deviance:   3098246  on 220  degrees of freedom
## AIC: 3100713
## 
## Number of Fisher Scoring iterations: 4
## 
## --------------------------------------------------------------------------------

Read the time series data for all the cases

Run on all the cases in Germany

tots.per.location(all.data,"Germany")
## [1] "GERMANY"
## Processing confirmed cases
## [1] "GERMANY"
## GERMANY  --  243305 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -49804 -25831  -3583  26784  48555 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -13492.23    3983.62  -3.387 0.000837 ***
## x.var         1279.46      30.98  41.305  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 29580 on 220 degrees of freedom
## Multiple R-squared:  0.8858, Adjusted R-squared:  0.8853 
## F-statistic:  1706 on 1 and 220 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.8443 -2.0526  0.0925  2.1057  3.3548 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.607112   0.307980   14.96   <2e-16 ***
## x.var       0.047447   0.002395   19.81   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.287 on 220 degrees of freedom
## Multiple R-squared:  0.6408, Adjusted R-squared:  0.6392 
## F-statistic: 392.6 on 1 and 220 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -307.50  -241.43   -21.13   141.07   215.97  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.033e+01  5.399e-04   19138   <2e-16 ***
## x.var       1.082e-02  3.329e-06    3251   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 19657517  on 221  degrees of freedom
## Residual deviance:  7592929  on 220  degrees of freedom
## AIC: 7595526
## 
## Number of Fisher Scoring iterations: 5
## 
## --------------------------------------------------------------------------------
## Processing death cases

## [1] "GERMANY"
## GERMANY  --  9300 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2443.38 -1323.14   -14.48  1352.36  2436.98 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1127.25     204.42  -5.514 9.78e-08 ***
## x.var          57.98       1.59  36.473  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1518 on 220 degrees of freedom
## Multiple R-squared:  0.8581, Adjusted R-squared:  0.8574 
## F-statistic:  1330 on 1 and 220 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3098 -1.8137 -0.1908  1.9304  3.0967 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.992509   0.271080   3.661 0.000314 ***
## x.var       0.049304   0.002108  23.391  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.013 on 220 degrees of freedom
## Multiple R-squared:  0.7132, Adjusted R-squared:  0.7119 
## F-statistic: 547.1 on 1 and 220 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -62.17  -49.58  -10.80   31.55   52.29  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 6.948e+00  2.791e-03  2489.3   <2e-16 ***
## x.var       1.211e-02  1.688e-05   717.3   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 985810  on 221  degrees of freedom
## Residual deviance: 380547  on 220  degrees of freedom
## AIC: 382312
## 
## Number of Fisher Scoring iterations: 6
## 
## --------------------------------------------------------------------------------
## Processing recovered cases

## [1] "GERMANY"
## GERMANY  --  215283 
## ===============================   running models...=============================== 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -46866 -21746    472  22731  39426 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -28949.89    3443.42  -8.407 5.27e-15 ***
## x.var         1227.13      26.78  45.831  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 25570 on 220 degrees of freedom
## Multiple R-squared:  0.9052, Adjusted R-squared:  0.9048 
## F-statistic:  2100 on 1 and 220 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   Linear Regression (lm): 
## 
## Call:
## lm(formula = y.var ~ x.var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6480 -2.1237 -0.4617  2.4148  3.8062 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.330878   0.325683   7.157 1.22e-11 ***
## x.var       0.059870   0.002532  23.641  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.418 on 220 degrees of freedom
## Multiple R-squared:  0.7176, Adjusted R-squared:  0.7163 
## F-statistic: 558.9 on 1 and 220 DF,  p-value: < 2.2e-16
## 
## -------------------------------------------------------------------------------- 
##   GLM using Family [1] "poisson" : 
## 
## Call:
## glm(formula = y.var ~ x.var, family = family)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -282.79  -208.73   -45.08   131.25   220.29  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 9.840e+00  6.385e-04   15412   <2e-16 ***
## x.var       1.283e-02  3.823e-06    3356   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 20398879  on 221  degrees of freedom
## Residual deviance:  6907816  on 220  degrees of freedom
## AIC: 6910183
## 
## Number of Fisher Scoring iterations: 6
## 
## --------------------------------------------------------------------------------

## [[1]]
## [[1]][[1]]
## [[1]][[1]][[1]]
## list()
## 
## [[1]][[1]][[2]]
##   geo.loc    Long 2020-01-22 2020-01-23 2020-01-24 2020-01-25 2020-01-26
## 1 GERMANY GERMANY          0          0          0          0          0
##   2020-01-27 2020-01-28 2020-01-29 2020-01-30 2020-01-31 2020-02-01 2020-02-02
## 1          1          4          4          4          5          8         10
##   2020-02-03 2020-02-04 2020-02-05 2020-02-06 2020-02-07 2020-02-08 2020-02-09
## 1         12         12         12         12         13         13         14
##   2020-02-10 2020-02-11 2020-02-12 2020-02-13 2020-02-14 2020-02-15 2020-02-16
## 1         14         16         16         16         16         16         16
##   2020-02-17 2020-02-18 2020-02-19 2020-02-20 2020-02-21 2020-02-22 2020-02-23
## 1         16         16         16         16         16         16         16
##   2020-02-24 2020-02-25 2020-02-26 2020-02-27 2020-02-28 2020-02-29 2020-03-01
## 1         16         17         27         46         48         79        130
##   2020-03-02 2020-03-03 2020-03-04 2020-03-05 2020-03-06 2020-03-07 2020-03-08
## 1        159        196        262        482        670        799       1040
##   2020-03-09 2020-03-10 2020-03-11 2020-03-12 2020-03-13 2020-03-14 2020-03-15
## 1       1176       1457       1908       2078       3675       4585       5795
##   2020-03-16 2020-03-17 2020-03-18 2020-03-19 2020-03-20 2020-03-21 2020-03-22
## 1       7272       9257      12327      15320      19848      22213      24873
##   2020-03-23 2020-03-24 2020-03-25 2020-03-26 2020-03-27 2020-03-28 2020-03-29
## 1      29056      32986      37323      43938      50871      57695      62095
##   2020-03-30 2020-03-31 2020-04-01 2020-04-02 2020-04-03 2020-04-04 2020-04-05
## 1      66885      71808      77872      84794      91159      96092     100123
##   2020-04-06 2020-04-07 2020-04-08 2020-04-09 2020-04-10 2020-04-11 2020-04-12
## 1     103374     107663     113296     118181     122171     124908     127854
##   2020-04-13 2020-04-14 2020-04-15 2020-04-16 2020-04-17 2020-04-18 2020-04-19
## 1     130072     131359     134753     137698     141397     143342     145184
##   2020-04-20 2020-04-21 2020-04-22 2020-04-23 2020-04-24 2020-04-25 2020-04-26
## 1     147065     148291     150648     153129     154999     156513     157770
##   2020-04-27 2020-04-28 2020-04-29 2020-04-30 2020-05-01 2020-05-02 2020-05-03
## 1     158758     159912     161539     163009     164077     164967     165664
##   2020-05-04 2020-05-05 2020-05-06 2020-05-07 2020-05-08 2020-05-09 2020-05-10
## 1     166152     167007     168162     169430     170588     171324     171879
##   2020-05-11 2020-05-12 2020-05-13 2020-05-14 2020-05-15 2020-05-16 2020-05-17
## 1     172576     173171     174098     174478     175233     175752     176369
##   2020-05-18 2020-05-19 2020-05-20 2020-05-21 2020-05-22 2020-05-23 2020-05-24
## 1     176551     177778     178473     179021     179710     179986     180328
##   2020-05-25 2020-05-26 2020-05-27 2020-05-28 2020-05-29 2020-05-30 2020-05-31
## 1     180600     181200     181524     182196     182922     183189     183410
##   2020-06-01 2020-06-02 2020-06-03 2020-06-04 2020-06-05 2020-06-06 2020-06-07
## 1     183594     183879     184121     184472     184924     185450     185750
##   2020-06-08 2020-06-09 2020-06-10 2020-06-11 2020-06-12 2020-06-13 2020-06-14
## 1     186109     186506     186522     186691     187226     187267     187518
##   2020-06-15 2020-06-16 2020-06-17 2020-06-18 2020-06-19 2020-06-20 2020-06-21
## 1     187682     188252     188604     189817     190299     190670     191272
##   2020-06-22 2020-06-23 2020-06-24 2020-06-25 2020-06-26 2020-06-27 2020-06-28
## 1     191768     192480     192871     193371     194036     194458     194693
##   2020-06-29 2020-06-30 2020-07-01 2020-07-02 2020-07-03 2020-07-04 2020-07-05
## 1     195042     195418     195893     196370     196780     197198     197523
##   2020-07-06 2020-07-07 2020-07-08 2020-07-09 2020-07-10 2020-07-11 2020-07-12
## 1     198064     198343     198699     199001     199332     199709     199919
##   2020-07-13 2020-07-14 2020-07-15 2020-07-16 2020-07-17 2020-07-18 2020-07-19
## 1     200180     200456     200890     201450     202045     202426     202735
##   2020-07-20 2020-07-21 2020-07-22 2020-07-23 2020-07-24 2020-07-25 2020-07-26
## 1     203325     203717     204276     204881     205623     206278     206667
##   2020-07-27 2020-07-28 2020-07-29 2020-07-30 2020-07-31 2020-08-01 2020-08-02
## 1     207112     207707     208546     209535     210399     211005     211220
##   2020-08-03 2020-08-04 2020-08-05 2020-08-06 2020-08-07 2020-08-08 2020-08-09
## 1     212111     212828     214113     215039     216196     216903     217288
##   2020-08-10 2020-08-11 2020-08-12 2020-08-13 2020-08-14 2020-08-15 2020-08-16
## 1     218508     219540     220859     222281     223791     224488     225007
##   2020-08-17 2020-08-18 2020-08-19 2020-08-20 2020-08-21 2020-08-22 2020-08-23
## 1     226700     228120     229706     231292     233029     233861     234494
##   2020-08-24 2020-08-25 2020-08-26 2020-08-27 2020-08-28 2020-08-29     NA
## 1     236122     237583     239010     240571     242126     242835 243305
##       NA
## 1 243305
## 
## 
## [[1]][[2]]
##   geo.loc    Long 2020-01-22 2020-01-23 2020-01-24 2020-01-25 2020-01-26
## 1 GERMANY GERMANY          0          0          0          0          0
##   2020-01-27 2020-01-28 2020-01-29 2020-01-30 2020-01-31 2020-02-01 2020-02-02
## 1          0          0          0          0          0          0          0
##   2020-02-03 2020-02-04 2020-02-05 2020-02-06 2020-02-07 2020-02-08 2020-02-09
## 1          0          0          0          0          0          0          0
##   2020-02-10 2020-02-11 2020-02-12 2020-02-13 2020-02-14 2020-02-15 2020-02-16
## 1          0          0          0          0          0          0          0
##   2020-02-17 2020-02-18 2020-02-19 2020-02-20 2020-02-21 2020-02-22 2020-02-23
## 1          0          0          0          0          0          0          0
##   2020-02-24 2020-02-25 2020-02-26 2020-02-27 2020-02-28 2020-02-29 2020-03-01
## 1          0          0          0          0          0          0          0
##   2020-03-02 2020-03-03 2020-03-04 2020-03-05 2020-03-06 2020-03-07 2020-03-08
## 1          0          0          0          0          0          0          0
##   2020-03-09 2020-03-10 2020-03-11 2020-03-12 2020-03-13 2020-03-14 2020-03-15
## 1          2          2          3          3          7          9         11
##   2020-03-16 2020-03-17 2020-03-18 2020-03-19 2020-03-20 2020-03-21 2020-03-22
## 1         17         24         28         44         67         84         94
##   2020-03-23 2020-03-24 2020-03-25 2020-03-26 2020-03-27 2020-03-28 2020-03-29
## 1        123        157        206        267        342        433        533
##   2020-03-30 2020-03-31 2020-04-01 2020-04-02 2020-04-03 2020-04-04 2020-04-05
## 1        645        775        920       1107       1275       1444       1584
##   2020-04-06 2020-04-07 2020-04-08 2020-04-09 2020-04-10 2020-04-11 2020-04-12
## 1       1810       2016       2349       2607       2767       2736       3022
##   2020-04-13 2020-04-14 2020-04-15 2020-04-16 2020-04-17 2020-04-18 2020-04-19
## 1       3194       3294       3804       4052       4352       4459       4586
##   2020-04-20 2020-04-21 2020-04-22 2020-04-23 2020-04-24 2020-04-25 2020-04-26
## 1       4862       5033       5279       5575       5760       5877       5976
##   2020-04-27 2020-04-28 2020-04-29 2020-04-30 2020-05-01 2020-05-02 2020-05-03
## 1       6126       6314       6467       6623       6736       6812       6866
##   2020-05-04 2020-05-05 2020-05-06 2020-05-07 2020-05-08 2020-05-09 2020-05-10
## 1       6993       6993       7275       7392       7510       7549       7569
##   2020-05-11 2020-05-12 2020-05-13 2020-05-14 2020-05-15 2020-05-16 2020-05-17
## 1       7661       7738       7861       7884       7897       7938       7962
##   2020-05-18 2020-05-19 2020-05-20 2020-05-21 2020-05-22 2020-05-23 2020-05-24
## 1       8003       8081       8144       8203       8228       8261       8283
##   2020-05-25 2020-05-26 2020-05-27 2020-05-28 2020-05-29 2020-05-30 2020-05-31
## 1       8309       8372       8428       8470       8504       8530       8540
##   2020-06-01 2020-06-02 2020-06-03 2020-06-04 2020-06-05 2020-06-06 2020-06-07
## 1       8555       8563       8602       8635       8658       8673       8685
##   2020-06-08 2020-06-09 2020-06-10 2020-06-11 2020-06-12 2020-06-13 2020-06-14
## 1       8695       8736       8752       8772       8783       8793       8801
##   2020-06-15 2020-06-16 2020-06-17 2020-06-18 2020-06-19 2020-06-20 2020-06-21
## 1       8807       8820       8851       8875       8887       8895       8895
##   2020-06-22 2020-06-23 2020-06-24 2020-06-25 2020-06-26 2020-06-27 2020-06-28
## 1       8899       8914       8928       8940       8965       8968       8968
##   2020-06-29 2020-06-30 2020-07-01 2020-07-02 2020-07-03 2020-07-04 2020-07-05
## 1       8976       8990       8995       9006       9010       9020       9023
##   2020-07-06 2020-07-07 2020-07-08 2020-07-09 2020-07-10 2020-07-11 2020-07-12
## 1       9022       9032       9046       9057       9063       9070       9071
##   2020-07-13 2020-07-14 2020-07-15 2020-07-16 2020-07-17 2020-07-18 2020-07-19
## 1       9074       9078       9080       9087       9088       9091       9092
##   2020-07-20 2020-07-21 2020-07-22 2020-07-23 2020-07-24 2020-07-25 2020-07-26
## 1       9094       9099       9102       9110       9120       9124       9124
##   2020-07-27 2020-07-28 2020-07-29 2020-07-30 2020-07-31 2020-08-01 2020-08-02
## 1       9125       9131       9135       9144       9147       9154       9154
##   2020-08-03 2020-08-04 2020-08-05 2020-08-06 2020-08-07 2020-08-08 2020-08-09
## 1       9154       9163       9179       9181       9195       9201       9202
##   2020-08-10 2020-08-11 2020-08-12 2020-08-13 2020-08-14 2020-08-15 2020-08-16
## 1       9203       9208       9213       9217       9230       9235       9235
##   2020-08-17 2020-08-18 2020-08-19 2020-08-20 2020-08-21 2020-08-22 2020-08-23
## 1       9236       9241       9249       9263       9266       9272       9275
##   2020-08-24 2020-08-25 2020-08-26 2020-08-27 2020-08-28 2020-08-29   NA   NA
## 1       9276       9281       9285       9290       9290       9299 9300 9300
## 
## 
## [[2]]
##   geo.loc    Long 2020-01-22 2020-01-23 2020-01-24 2020-01-25 2020-01-26
## 1 GERMANY GERMANY          0          0          0          0          0
##   2020-01-27 2020-01-28 2020-01-29 2020-01-30 2020-01-31 2020-02-01 2020-02-02
## 1          0          0          0          0          0          0          0
##   2020-02-03 2020-02-04 2020-02-05 2020-02-06 2020-02-07 2020-02-08 2020-02-09
## 1          0          0          0          0          0          0          0
##   2020-02-10 2020-02-11 2020-02-12 2020-02-13 2020-02-14 2020-02-15 2020-02-16
## 1          0          0          0          1          1          1          1
##   2020-02-17 2020-02-18 2020-02-19 2020-02-20 2020-02-21 2020-02-22 2020-02-23
## 1          1         12         12         12         14         14         14
##   2020-02-24 2020-02-25 2020-02-26 2020-02-27 2020-02-28 2020-02-29 2020-03-01
## 1         14         14         15         16         16         16         16
##   2020-03-02 2020-03-03 2020-03-04 2020-03-05 2020-03-06 2020-03-07 2020-03-08
## 1         16         16         16         16         17         18         18
##   2020-03-09 2020-03-10 2020-03-11 2020-03-12 2020-03-13 2020-03-14 2020-03-15
## 1         18         18         25         25         46         46         46
##   2020-03-16 2020-03-17 2020-03-18 2020-03-19 2020-03-20 2020-03-21 2020-03-22
## 1         67         67        105        113        180        233        266
##   2020-03-23 2020-03-24 2020-03-25 2020-03-26 2020-03-27 2020-03-28 2020-03-29
## 1        266       3243       3547       5673       6658       8481       9211
##   2020-03-30 2020-03-31 2020-04-01 2020-04-02 2020-04-03 2020-04-04 2020-04-05
## 1      13500      16100      18700      22440      24575      26400      28700
##   2020-04-06 2020-04-07 2020-04-08 2020-04-09 2020-04-10 2020-04-11 2020-04-12
## 1      28700      36081      46300      52407      53913      57400      60300
##   2020-04-13 2020-04-14 2020-04-15 2020-04-16 2020-04-17 2020-04-18 2020-04-19
## 1      64300      68200      72600      77000      83114      85400      88000
##   2020-04-20 2020-04-21 2020-04-22 2020-04-23 2020-04-24 2020-04-25 2020-04-26
## 1      91500      95200      99400     103300     109800     109800     112000
##   2020-04-27 2020-04-28 2020-04-29 2020-04-30 2020-05-01 2020-05-02 2020-05-03
## 1     114500     117400     120400     123500     126900     129000     130600
##   2020-05-04 2020-05-05 2020-05-06 2020-05-07 2020-05-08 2020-05-09 2020-05-10
## 1     132700     135100     139900     141700     141700     143300     144400
##   2020-05-11 2020-05-12 2020-05-13 2020-05-14 2020-05-15 2020-05-16 2020-05-17
## 1     145617     147200     148700     150300     151597     152600     154011
##   2020-05-18 2020-05-19 2020-05-20 2020-05-21 2020-05-22 2020-05-23 2020-05-24
## 1     155041     155681     156966     158087     159064     159716     160281
##   2020-05-25 2020-05-26 2020-05-27 2020-05-28 2020-05-29 2020-05-30 2020-05-31
## 1     161199     161967     162820     163360     164245     164908     165352
##   2020-06-01 2020-06-02 2020-06-03 2020-06-04 2020-06-05 2020-06-06 2020-06-07
## 1     165632     166609     167453     167909     168480     168958     169224
##   2020-06-08 2020-06-09 2020-06-10 2020-06-11 2020-06-12 2020-06-13 2020-06-14
## 1     169556     170129     170630     170961     171535     171970     172089
##   2020-06-15 2020-06-16 2020-06-17 2020-06-18 2020-06-19 2020-06-20 2020-06-21
## 1     172692     172842     173599     173847     173972     174609     174740
##   2020-06-22 2020-06-23 2020-06-24 2020-06-25 2020-06-26 2020-06-27 2020-06-28
## 1     175143     175825     176422     176764     177149     177518     177657
##   2020-06-29 2020-06-30 2020-07-01 2020-07-02 2020-07-03 2020-07-04 2020-07-05
## 1     177770     178100     179100     179800     180300     181000     181719
##   2020-07-06 2020-07-07 2020-07-08 2020-07-09 2020-07-10 2020-07-11 2020-07-12
## 1     182160     182661     183153     183728     184028     184266     184414
##   2020-07-13 2020-07-14 2020-07-15 2020-07-16 2020-07-17 2020-07-18 2020-07-19
## 1     185100     185100     186000     186400     186900     187200     187400
##   2020-07-20 2020-07-21 2020-07-22 2020-07-23 2020-07-24 2020-07-25 2020-07-26
## 1     188070     188221     188628     189140     189696     189919     190055
##   2020-07-27 2020-07-28 2020-07-29 2020-07-30 2020-07-31 2020-08-01 2020-08-02
## 1     190314     190711     191279     191551     191992     192636     192908
##   2020-08-03 2020-08-04 2020-08-05 2020-08-06 2020-08-07 2020-08-08 2020-08-09
## 1     193594     194173     194568     195281     195935     196550     196783
##   2020-08-10 2020-08-11 2020-08-12 2020-08-13 2020-08-14 2020-08-15 2020-08-16
## 1     197382     198347     198991     199654     200440     200756     201187
##   2020-08-17 2020-08-18 2020-08-19 2020-08-20 2020-08-21 2020-08-22 2020-08-23
## 1     202249     203677     204454     205359     206656     207606     207985
##   2020-08-24 2020-08-25 2020-08-26 2020-08-27 2020-08-28 2020-08-29     NA
## 1     208653     210333     211691     212909     214186     214790 215283
##       NA
## 1 215283

Total for death cases for “ALL” the regions

Read time series data for confirmed cases

Compute changes and growth rates per location for all the countries

Compute changes and growth rates per location for ‘India’ and ‘Germany’

growth.rate(TS.data,geo.loc=c("India","Germany"))
## [1] "INDIA"
## [1] "GERMANY"
## Processing...  INDIA

## Processing...  GERMANY
## Loading required package: pheatmap
## Loading required package: gplots
## 
## Attaching package: 'gplots'
## The following object is masked from 'package:stats':
## 
##     lowess

## $Changes
##   geo.loc 2020-01-23 2020-01-24 2020-01-25 2020-01-26 2020-01-27 2020-01-28
## 1   INDIA          0          0          0          0          0          0
## 2 GERMANY          0          0          0          0          1          3
##   2020-01-29 2020-01-30 2020-01-31 2020-02-01 2020-02-02 2020-02-03 2020-02-04
## 1          0          1          0          0          1          1          0
## 2          0          0          1          3          2          2          0
##   2020-02-05 2020-02-06 2020-02-07 2020-02-08 2020-02-09 2020-02-10 2020-02-11
## 1          0          0          0          0          0          0          0
## 2          0          0          1          0          1          0          2
##   2020-02-12 2020-02-13 2020-02-14 2020-02-15 2020-02-16 2020-02-17 2020-02-18
## 1          0          0          0          0          0          0          0
## 2          0          0          0          0          0          0          0
##   2020-02-19 2020-02-20 2020-02-21 2020-02-22 2020-02-23 2020-02-24 2020-02-25
## 1          0          0          0          0          0          0          0
## 2          0          0          0          0          0          0          1
##   2020-02-26 2020-02-27 2020-02-28 2020-02-29 2020-03-01 2020-03-02 2020-03-03
## 1          0          0          0          0          0          2          0
## 2         10         19          2         31         51         29         37
##   2020-03-04 2020-03-05 2020-03-06 2020-03-07 2020-03-08 2020-03-09 2020-03-10
## 1         23          2          1          3          5          4         13
## 2         66        220        188        129        241        136        281
##   2020-03-11 2020-03-12 2020-03-13 2020-03-14 2020-03-15 2020-03-16 2020-03-17
## 1          6         11          9         20         11          6         23
## 2        451        170       1597        910       1210       1477       1985
##   2020-03-18 2020-03-19 2020-03-20 2020-03-21 2020-03-22 2020-03-23 2020-03-24
## 1         14         38         50         86         66        103         37
## 2       3070       2993       4528       2365       2660       4183       3930
##   2020-03-25 2020-03-26 2020-03-27 2020-03-28 2020-03-29 2020-03-30 2020-03-31
## 1        121         70        160        100         37        227        146
## 2       4337       6615       6933       6824       4400       4790       4923
##   2020-04-01 2020-04-02 2020-04-03 2020-04-04 2020-04-05 2020-04-06 2020-04-07
## 1        601        545         24        515        506       1190        533
## 2       6064       6922       6365       4933       4031       3251       4289
##   2020-04-08 2020-04-09 2020-04-10 2020-04-11 2020-04-12 2020-04-13 2020-04-14
## 1        605        809        873        848        759       1248       1034
## 2       5633       4885       3990       2737       2946       2218       1287
##   2020-04-15 2020-04-16 2020-04-17 2020-04-18 2020-04-19 2020-04-20 2020-04-21
## 1        835       1108        922       1370       1893        924       1541
## 2       3394       2945       3699       1945       1842       1881       1226
##   2020-04-22 2020-04-23 2020-04-24 2020-04-25 2020-04-26 2020-04-27 2020-04-28
## 1       1290       1707       1453       1753       1607       1561       1873
## 2       2357       2481       1870       1514       1257        988       1154
##   2020-04-29 2020-04-30 2020-05-01 2020-05-02 2020-05-03 2020-05-04 2020-05-05
## 1       1738       1801       2394       2442       2806       3932       2963
## 2       1627       1470       1068        890        697        488        855
##   2020-05-06 2020-05-07 2020-05-08 2020-05-09 2020-05-10 2020-05-11 2020-05-12
## 1       3587       3364       3344       3113       4353       3607       3524
## 2       1155       1268       1158        736        555        697        595
##   2020-05-13 2020-05-14 2020-05-15 2020-05-16 2020-05-17 2020-05-18 2020-05-19
## 1       3763       3942       3787       4864       5050       4630       6147
## 2        927        380        755        519        617        182       1227
##   2020-05-20 2020-05-21 2020-05-22 2020-05-23 2020-05-24 2020-05-25 2020-05-26
## 1       5553       6198       6568       6629       7113       6414       5843
## 2        695        548        689        276        342        272        600
##   2020-05-27 2020-05-28 2020-05-29 2020-05-30 2020-05-31 2020-06-01 2020-06-02
## 1       7293       7300       8105       8336       8782       7761       8821
## 2        324        672        726        267        221        184        285
##   2020-06-03 2020-06-04 2020-06-05 2020-06-06 2020-06-07 2020-06-08 2020-06-09
## 1       9633       9889       9471      10438      10864       8442      10218
## 2        242        351        452        526        300        359        397
##   2020-06-10 2020-06-11 2020-06-12 2020-06-13 2020-06-14 2020-06-15 2020-06-16
## 1      10459      10930      11458      11929      11502      10667      10974
## 2         16        169        535         41        251        164        570
##   2020-06-17 2020-06-18 2020-06-19 2020-06-20 2020-06-21 2020-06-22 2020-06-23
## 1      12881      13586      14516      15403      14831      14933      15968
## 2        352       1213        482        371        602        496        712
##   2020-06-24 2020-06-25 2020-06-26 2020-06-27 2020-06-28 2020-06-29 2020-06-30
## 1      16922      17296      18552      19906      19459      18522      18641
## 2        391        500        665        422        235        349        376
##   2020-07-01 2020-07-02 2020-07-03 2020-07-04 2020-07-05 2020-07-06 2020-07-07
## 1      19160      20903      22771      24850      24248      22251      22753
## 2        475        477        410        418        325        541        279
##   2020-07-08 2020-07-09 2020-07-10 2020-07-11 2020-07-12 2020-07-13 2020-07-14
## 1      24879      26506      27114      28606      28732      28498      29429
## 2        356        302        331        377        210        261        276
##   2020-07-15 2020-07-16 2020-07-17 2020-07-18 2020-07-19 2020-07-20 2020-07-21
## 1      32676      34975      35252      38697      40425      37132      37740
## 2        434        560        595        381        309        590        392
##   2020-07-22 2020-07-23 2020-07-24 2020-07-25 2020-07-26 2020-07-27 2020-07-28
## 1      45720      49310      48916      48611      49981      44457      51596
## 2        559        605        742        655        389        445        595
##   2020-07-29 2020-07-30 2020-07-31 2020-08-01 2020-08-02 2020-08-03 2020-08-04
## 1      50294      52783      61242      54735      52972      52050      52509
## 2        839        989        864        606        215        891        717
##   2020-08-05 2020-08-06 2020-08-07 2020-08-08 2020-08-09 2020-08-10 2020-08-11
## 1      56282      62538      61537      64399      62064      53601      60963
## 2       1285        926       1157        707        385       1220       1032
##   2020-08-12 2020-08-13 2020-08-14 2020-08-15 2020-08-16 2020-08-17 2020-08-18
## 1      66999      64553      64732      64030      57711      55018      64572
## 2       1319       1422       1510        697        519       1693       1420
##   2020-08-19 2020-08-20 2020-08-21 2020-08-22 2020-08-23 2020-08-24 2020-08-25
## 1      69672      68900      69876      69239      61408      60975      57224
## 2       1586       1586       1737        832        633       1628       1461
##   2020-08-26 2020-08-27 2020-08-28 2020-08-29 2020-08-30
## 1      85687      77266      76472      78761      78512
## 2       1427       1561       1555        709        470
## 
## $Growth.Rate
##   geo.loc 2020-01-24 2020-01-25 2020-01-26 2020-01-27 2020-01-28 2020-01-29
## 1   INDIA        NaN        NaN        NaN        NaN        NaN        NaN
## 2 GERMANY        NaN        NaN        NaN         NA          3          0
##   2020-01-30 2020-01-31 2020-02-01 2020-02-02 2020-02-03 2020-02-04 2020-02-05
## 1         NA          0        NaN         NA          1          0        NaN
## 2        NaN         NA          3  0.6666667          1          0        NaN
##   2020-02-06 2020-02-07 2020-02-08 2020-02-09 2020-02-10 2020-02-11 2020-02-12
## 1        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 2        NaN         NA          0         NA          0         NA          0
##   2020-02-13 2020-02-14 2020-02-15 2020-02-16 2020-02-17 2020-02-18 2020-02-19
## 1        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 2        NaN        NaN        NaN        NaN        NaN        NaN        NaN
##   2020-02-20 2020-02-21 2020-02-22 2020-02-23 2020-02-24 2020-02-25 2020-02-26
## 1        NaN        NaN        NaN        NaN        NaN        NaN        NaN
## 2        NaN        NaN        NaN        NaN        NaN         NA         10
##   2020-02-27 2020-02-28 2020-02-29 2020-03-01 2020-03-02 2020-03-03 2020-03-04
## 1        NaN        NaN        NaN        NaN         NA   0.000000         NA
## 2        1.9  0.1052632       15.5   1.645161  0.5686275   1.275862   1.783784
##   2020-03-05 2020-03-06 2020-03-07 2020-03-08 2020-03-09 2020-03-10 2020-03-11
## 1 0.08695652  0.5000000  3.0000000   1.666667  0.8000000   3.250000  0.4615385
## 2 3.33333333  0.8545455  0.6861702   1.868217  0.5643154   2.066176  1.6049822
##   2020-03-12 2020-03-13 2020-03-14 2020-03-15 2020-03-16 2020-03-17 2020-03-18
## 1  1.8333333  0.8181818  2.2222222    0.55000  0.5454545   3.833333  0.6086957
## 2  0.3769401  9.3941176  0.5698184    1.32967  1.2206612   1.343940  1.5465995
##   2020-03-19 2020-03-20 2020-03-21 2020-03-22 2020-03-23 2020-03-24 2020-03-25
## 1  2.7142857   1.315789  1.7200000  0.7674419   1.560606  0.3592233   3.270270
## 2  0.9749186   1.512863  0.5223057  1.1247357   1.572556  0.9395171   1.103562
##   2020-03-26 2020-03-27 2020-03-28 2020-03-29 2020-03-30 2020-03-31 2020-04-01
## 1  0.5785124   2.285714  0.6250000  0.3700000   6.135135  0.6431718   4.116438
## 2  1.5252479   1.048073  0.9842781  0.6447831   1.088636  1.0277662   1.231769
##   2020-04-02 2020-04-03 2020-04-04 2020-04-05 2020-04-06 2020-04-07 2020-04-08
## 1   0.906822  0.0440367 21.4583333  0.9825243  2.3517787  0.4478992   1.135084
## 2   1.141491  0.9195319  0.7750196  0.8171498  0.8064996  1.3192864   1.313360
##   2020-04-09 2020-04-10 2020-04-11 2020-04-12 2020-04-13 2020-04-14 2020-04-15
## 1  1.3371901  1.0791100  0.9713631  0.8950472  1.6442688  0.8285256  0.8075435
## 2  0.8672111  0.8167861  0.6859649  1.0763610  0.7528853  0.5802525  2.6371406
##   2020-04-16 2020-04-17 2020-04-18 2020-04-19 2020-04-20 2020-04-21 2020-04-22
## 1  1.3269461   0.832130  1.4859002  1.3817518  0.4881141   1.667749  0.8371188
## 2  0.8677077   1.256027  0.5258178  0.9470437  1.0211726   0.651781  1.9225122
##   2020-04-23 2020-04-24 2020-04-25 2020-04-26 2020-04-27 2020-04-28 2020-04-29
## 1   1.323256  0.8512009  1.2064694  0.9167142  0.9713752   1.199872  0.9279231
## 2   1.052609  0.7537283  0.8096257  0.8302510  0.7859984   1.168016  1.4098787
##   2020-04-30 2020-05-01 2020-05-02 2020-05-03 2020-05-04 2020-05-05 2020-05-06
## 1  1.0362486  1.3292615  1.0200501  1.1490581  1.4012830  0.7535605   1.210597
## 2  0.9035034  0.7265306  0.8333333  0.7831461  0.7001435  1.7520492   1.350877
##   2020-05-07 2020-05-08 2020-05-09 2020-05-10 2020-05-11 2020-05-12 2020-05-13
## 1  0.9378311  0.9940547  0.9309211  1.3983296  0.8286239  0.9769892   1.067821
## 2  1.0978355  0.9132492  0.6355786  0.7540761  1.2558559  0.8536585   1.557983
##   2020-05-14 2020-05-15 2020-05-16 2020-05-17 2020-05-18 2020-05-19 2020-05-20
## 1  1.0475684  0.9606799  1.2843940   1.038240  0.9168317   1.327646  0.9033675
## 2  0.4099245  1.9868421  0.6874172   1.188825  0.2949757   6.741758  0.5664222
##   2020-05-21 2020-05-22 2020-05-23 2020-05-24 2020-05-25 2020-05-26 2020-05-27
## 1  1.1161534   1.059697  1.0092875   1.073013  0.9017292   0.910976    1.24816
## 2  0.7884892   1.257299  0.4005806   1.239130  0.7953216   2.205882    0.54000
##   2020-05-28 2020-05-29 2020-05-30 2020-05-31 2020-06-01 2020-06-02 2020-06-03
## 1   1.000960   1.110274  1.0285009  1.0535029  0.8837395   1.136580  1.0920531
## 2   2.074074   1.080357  0.3677686  0.8277154  0.8325792   1.548913  0.8491228
##   2020-06-04 2020-06-05 2020-06-06 2020-06-07 2020-06-08 2020-06-09 2020-06-10
## 1   1.026575  0.9577308   1.102101  1.0408124  0.7770619   1.210377 1.02358583
## 2   1.450413  1.2877493   1.163717  0.5703422  1.1966667   1.105850 0.04030227
##   2020-06-11 2020-06-12 2020-06-13 2020-06-14 2020-06-15 2020-06-16 2020-06-17
## 1   1.045033   1.048307 1.04110665  0.9642049  0.9274039    1.02878  1.1737744
## 2  10.562500   3.165680 0.07663551  6.1219512  0.6533865    3.47561  0.6175439
##   2020-06-18 2020-06-19 2020-06-20 2020-06-21 2020-06-22 2020-06-23 2020-06-24
## 1   1.054732  1.0684528  1.0611050  0.9628644  1.0068775   1.069310  1.0597445
## 2   3.446023  0.3973619  0.7697095  1.6226415  0.8239203   1.435484  0.5491573
##   2020-06-25 2020-06-26 2020-06-27 2020-06-28 2020-06-29 2020-06-30 2020-07-01
## 1   1.022101   1.072618  1.0729840  0.9775445  0.9518475   1.006425   1.027842
## 2   1.278772   1.330000  0.6345865  0.5568720  1.4851064   1.077364   1.263298
##   2020-07-02 2020-07-03 2020-07-04 2020-07-05 2020-07-06 2020-07-07 2020-07-08
## 1   1.090971  1.0893652   1.091300  0.9757746  0.9176427  1.0225608   1.093438
## 2   1.004211  0.8595388   1.019512  0.7775120  1.6646154  0.5157116   1.275986
##   2020-07-09 2020-07-10 2020-07-11 2020-07-12 2020-07-13 2020-07-14 2020-07-15
## 1  1.0653965   1.022938   1.055027  1.0044047  0.9918558   1.032669   1.110333
## 2  0.8483146   1.096026   1.138973  0.5570292  1.2428571   1.057471   1.572464
##   2020-07-16 2020-07-17 2020-07-18 2020-07-19 2020-07-20 2020-07-21 2020-07-22
## 1   1.070357    1.00792  1.0977250  1.0446546  0.9185405  1.0163740   1.211447
## 2   1.290323    1.06250  0.6403361  0.8110236  1.9093851  0.6644068   1.426020
##   2020-07-23 2020-07-24 2020-07-25 2020-07-26 2020-07-27 2020-07-28 2020-07-29
## 1   1.078521  0.9920097  0.9937648  1.0281829   0.889478   1.160582  0.9747655
## 2   1.082290  1.2264463  0.8827493  0.5938931   1.143959   1.337079  1.4100840
##   2020-07-30 2020-07-31 2020-08-01 2020-08-02 2020-08-03 2020-08-04 2020-08-05
## 1   1.049489  1.1602599  0.8937494  0.9677903  0.9825946  1.0088184   1.071854
## 2   1.178784  0.8736097  0.7013889  0.3547855  4.1441860  0.8047138   1.792190
##   2020-08-06 2020-08-07 2020-08-08 2020-08-09 2020-08-10 2020-08-11 2020-08-12
## 1  1.1111545  0.9839937  1.0465086  0.9637417  0.8636408  1.1373482   1.099011
## 2  0.7206226  1.2494600  0.6110631  0.5445545  3.1688312  0.8459016   1.278101
##   2020-08-13 2020-08-14 2020-08-15 2020-08-16 2020-08-17 2020-08-18 2020-08-19
## 1   0.963492   1.002773  0.9891553  0.9013119  0.9533365  1.1736523   1.078982
## 2   1.078089   1.061885  0.4615894  0.7446198  3.2620424  0.8387478   1.116901
##   2020-08-20 2020-08-21 2020-08-22 2020-08-23 2020-08-24 2020-08-25 2020-08-26
## 1  0.9889195   1.014165  0.9908839  0.8868990  0.9929488  0.9384830  1.4973962
## 2  1.0000000   1.095208  0.4789868  0.7608173  2.5718799  0.8974201  0.9767283
##   2020-08-27 2020-08-28 2020-08-29 2020-08-30 NA
## 1  0.9017237  0.9897238  1.0299325  0.9968385 NA
## 2  1.0939033  0.9961563  0.4559486  0.6629055 NA

Obtain Time Series data

Explore different combinations of regions/cities/countries When combining different locations, heatmaps will also be generated comparing the trends among these locations

Retrieve time series data

Static and interactive plot

totals.plt(TS.data)
## Loading required package: plotly
## Loading required package: ggplot2
## Warning: package 'ggplot2' was built under R version 4.0.2
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
## Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
## Please use `arrange()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.

Totals for Germany, without displaying totals and one plot per page

totals.plt(TS.data, c("Germany"), with.totals=FALSE,one.plt.per.page=TRUE)
## [1] "GERMANY"
## Warning in par(new = TRUE): calling par(new=TRUE) with no plot

## A line object has been specified, but lines is not in the mode
## Adding lines to the mode...

totals.plt(TS.data, c("India"), with.totals=FALSE,one.plt.per.page=TRUE)
## [1] "INDIA"

## A line object has been specified, but lines is not in the mode
## Adding lines to the mode...

Totals for Germany, India; including global totals with the linear and semi-log plots arranged one next to the other

totals.plt(TS.data, c("Germany","India"), with.totals=TRUE,one.plt.per.page=FALSE)
## Warning in if (toupper(geo.loc0) != "ALL") {: the condition has length > 1 and
## only the first element will be used
## [1] "GERMANY"
## [1] "INDIA"
## A line object has been specified, but lines is not in the mode
## Adding lines to the mode...
## A line object has been specified, but lines is not in the mode
## Adding lines to the mode...

Totals for all the locations reported on the dataset, interactive plot will be saved as “totals-all.html”

totals.plt(TS.data, "ALL", fileName="totals-all")

# retrieve aggregated data
data <- covid19.data("aggregated")

Interactive map of aggregated cases – with more spatial resolution

live.map(data)

Interactive map of the time series data of thae confirmed cases with less spatial resolution, ie. aggregated by country

live.map(covid19.data("ts-confirmed"))
## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 
## --------------------------------------------------------------------------------

Read time series data for confirmed cases

Run a SIR model for a given geographical location

generate.SIR.model(data,"Germany",tot.population=83149300)
## ################################################################################
## This is an experimental feature, being currently under active development!
## Please check the development version of the package for the latest updates on it
## ################################################################################ 
## [1] "GERMANY"
## Processing...  GERMANY 
##   [1]      0      0      0      0      0      1      4      4      4      5
##  [11]      8     10     12     12     12     12     13     13     14     14
##  [21]     16     16     16     16     16     16     16     16     16     16
##  [31]     16     16     16     16     17     27     46     48     79    130
##  [41]    159    196    262    482    670    799   1040   1176   1457   1908
##  [51]   2078   3675   4585   5795   7272   9257  12327  15320  19848  22213
##  [61]  24873  29056  32986  37323  43938  50871  57695  62095  66885  71808
##  [71]  77872  84794  91159  96092 100123 103374 107663 113296 118181 122171
##  [81] 124908 127854 130072 131359 134753 137698 141397 143342 145184 147065
##  [91] 148291 150648 153129 154999 156513 157770 158758 159912 161539 163009
## [101] 164077 164967 165664 166152 167007 168162 169430 170588 171324 171879
## [111] 172576 173171 174098 174478 175233 175752 176369 176551 177778 178473
## [121] 179021 179710 179986 180328 180600 181200 181524 182196 182922 183189
## [131] 183410 183594 183879 184121 184472 184924 185450 185750 186109 186506
## [141] 186522 186691 187226 187267 187518 187682 188252 188604 189817 190299
## [151] 190670 191272 191768 192480 192871 193371 194036 194458 194693 195042
## [161] 195418 195893 196370 196780 197198 197523 198064 198343 198699 199001
## [171] 199332 199709 199919 200180 200456 200890 201450 202045 202426 202735
## [181] 203325 203717 204276 204881 205623 206278 206667 207112 207707 208546
## [191] 209535 210399 211005 211220 212111 212828 214113 215039 216196 216903
## [201] 217288 218508 219540 220859 222281 223791 224488 225007 226700 228120
## [211] 229706 231292 233029 233861 234494 236122 237583 239010 240571 242126
## [221] 242835 243305
## [1] 36
##  [1]    27    46    48    79   130   159   196   262   482   670   799  1040
## [13]  1176  1457  1908  2078  3675  4585  5795  7272  9257 12327 15320 19848
## [25] 22213 24873
## ------------------------  Parameters used to create model ------------------------ 
##      Region: GERMANY 
##      Time interval to consider: t0=36 - t1= ; tfinal=90 
##          t0: 2020-02-27 -- t1:  
##      Number of days considered for initial guess: 26 
##      Fatality rate: 0.02 
##      Population of the region: 83149300 
## --------------------------------------------------------------------------------
## Loading required package: deSolve
## [1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH"
##      beta     gamma 
## 0.6398335 0.3601671 
##   R0 = 1.77649087472918 
##   Max nbr of infected: 9430798.58  ( 11.34 %) 
##   Max nbr of casualties, assuming  2% fatality rate: 188615.97 
##   Max reached at day : 53 ==>  2020-04-20 
## ================================================================================

## $Infected
##  [1]    27    46    48    79   130   159   196   262   482   670   799  1040
## [13]  1176  1457  1908  2078  3675  4585  5795  7272  9257 12327 15320 19848
## [25] 22213 24873
## 
## $model
##    time        S            I            R
## 1     1 83149273 2.700000e+01 0.000000e+00
## 2     2 83149253 3.571258e+01 1.122047e+01
## 3     3 83149227 4.723660e+01 2.606164e+01
## 4     4 83149192 6.247927e+01 4.569189e+01
## 5     5 83149146 8.264057e+01 7.165661e+01
## 6     6 83149085 1.093076e+02 1.059997e+02
## 7     7 83149004 1.445796e+02 1.514250e+02
## 8     8 83148897 1.912333e+02 2.115083e+02
## 9     9 83148756 2.529412e+02 2.909796e+02
## 10   10 83148569 3.345609e+02 3.960949e+02
## 11   11 83148322 4.425171e+02 5.351290e+02
## 12   12 83147996 5.853073e+02 7.190265e+02
## 13   13 83147564 7.741705e+02 9.622632e+02
## 14   14 83146992 1.023971e+03 1.283985e+03
## 15   15 83146236 1.354367e+03 1.709516e+03
## 16   16 83145236 1.791357e+03 2.272347e+03
## 17   17 83143914 2.369323e+03 3.016773e+03
## 18   18 83142165 3.133726e+03 4.001377e+03
## 19   19 83139852 4.144682e+03 5.303629e+03
## 20   20 83136792 5.481665e+03 7.025976e+03
## 21   21 83132746 7.249732e+03 9.303882e+03
## 22   22 83127396 9.587729e+03 1.231646e+04
## 23   23 83120320 1.267912e+04 1.630047e+04
## 24   24 83110965 1.676621e+04 2.156890e+04
## 25   25 83098596 2.216893e+04 2.853530e+04
## 26   26 83082245 2.930940e+04 3.774604e+04
## 27   27 83060633 3.874415e+04 4.992262e+04
## 28   28 83032077 5.120613e+04 6.601731e+04
## 29   29 82994355 6.765934e+04 8.728614e+04
## 30   30 82944547 8.936927e+04 1.153842e+05
## 31   31 82878817 1.179932e+05 1.524900e+05
## 32   32 82792140 1.556942e+05 2.014660e+05
## 33   33 82677951 2.052836e+05 2.660658e+05
## 34   34 82527708 2.703939e+05 3.511980e+05
## 35   35 82330360 3.556823e+05 4.632572e+05
## 36   36 82071709 4.670573e+05 6.105340e+05
## 37   37 81733684 6.119088e+05 8.037068e+05
## 38   38 81293587 7.993003e+05 1.056413e+06
## 39   39 80723382 1.040049e+06 1.385869e+06
## 40   40 79989244 1.346568e+06 1.813487e+06
## 41   41 79051649 1.732293e+06 2.365358e+06
## 42   42 77866431 2.210447e+06 3.072422e+06
## 43   43 76387361 2.791898e+06 3.970041e+06
## 44   44 74570742 3.481959e+06 5.096600e+06
## 45   45 72382268 4.276247e+06 6.490785e+06
## 46   46 69805668 5.156334e+06 8.187298e+06
## 47   47 66851604 6.086538e+06 1.021116e+07
## 48   48 63564164 7.013795e+06 1.257134e+07
## 49   49 60021892 7.872222e+06 1.525519e+07
## 50   50 56331366 8.592577e+06 1.822536e+07
## 51   51 52613901 9.114587e+06 2.142081e+07
## 52   52 48988800 9.398318e+06 2.476218e+07
## 53   53 45558037 9.430799e+06 2.816046e+07
## 54   54 42396339 9.226022e+06 3.152694e+07
## 55   55 39548104 8.819214e+06 3.478198e+07
## 56   56 37030079 8.258043e+06 3.786118e+07
## 57   57 34837373 7.593758e+06 4.071817e+07
## 58   58 32950410 6.874266e+06 4.332462e+07
## 59   59 31341191 6.139918e+06 4.566819e+07
## 60   60 29978130 5.421772e+06 4.774940e+07
## 61   61 28829350 4.741672e+06 4.957828e+07
## 62   62 27864693 4.113376e+06 5.117123e+07
## 63   63 27056770 3.544138e+06 5.254839e+07
## 64   64 26381374 3.036341e+06 5.373158e+07
## 65   65 25817507 2.588959e+06 5.474283e+07
## 66   66 25347179 2.198752e+06 5.560337e+07
## 67   67 24955119 1.861188e+06 5.633299e+07
## 68   68 24628441 1.571110e+06 5.694975e+07
## 69   69 24356321 1.323199e+06 5.746978e+07
## 70   70 24129690 1.112276e+06 5.790733e+07
## 71   71 23940967 9.334862e+05 5.827485e+07
## 72   72 23783824 7.823965e+05 5.858308e+07
## 73   73 23652982 6.550373e+05 5.884128e+07
## 74   74 23544043 5.479054e+05 5.905735e+07
## 75   75 23453341 4.579442e+05 5.923802e+07
## 76   76 23377823 3.825097e+05 5.938897e+07
## 77   77 23314948 3.193316e+05 5.951502e+07
## 78   78 23262599 2.664706e+05 5.962023e+07
## 79   79 23219014 2.222781e+05 5.970801e+07
## 80   80 23182725 1.853579e+05 5.978122e+07
## 81   81 23152511 1.545307e+05 5.984226e+07
## 82   82 23127355 1.288030e+05 5.989314e+07
## 83   83 23106409 1.073398e+05 5.993555e+07
## 84   84 23088970 8.943988e+04 5.997089e+07
## 85   85 23074450 7.451583e+04 6.000033e+07
## 86   86 23062360 6.207568e+04 6.002486e+07
## 87   87 23052294 5.170798e+04 6.004530e+07
## 88   88 23043913 4.306881e+04 6.006232e+07
## 89   89 23036934 3.587092e+04 6.007649e+07
## 90   90 23031124 2.987452e+04 6.008830e+07
## 
## $params
## $params$beta
##      beta 
## 0.6398335 
## 
## $params$gamma
##     gamma 
## 0.3601671 
## 
## $params$R0
##       R0 
## 1.776491

Modelling the spread for the whole world, storing the model and generating an interactive visualization

world.SIR.model <- generate.SIR.model(data,"ALL", t0=1,t1=15, tot.population=7.8e9, staticPlt=FALSE)
## ################################################################################
## This is an experimental feature, being currently under active development!
## Please check the development version of the package for the latest updates on it
## ################################################################################ 
## [1] "ALL"
## Processing...  ALL 
##  [1]   555   654   941  1434  2118  2927  5578  6166  8234  9926 12038 16787
## [13] 19887 23898 27643
## ------------------------  Parameters used to create model ------------------------ 
##      Region: ALL 
##      Time interval to consider: t0=1 - t1=15 ; tfinal=90 
##          t0: 2020-01-23 -- t1: 2020-02-06 
##      Number of days considered for initial guess: 15 
##      Fatality rate: 0.02 
##      Population of the region: 7.8e+09 
## -------------------------------------------------------------------------------- 
## [1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH"
##      beta     gamma 
## 0.6442089 0.3557911 
##   R0 = 1.81063807547213 
##   Max nbr of infected: 932010930.62  ( 11.95 %) 
##   Max nbr of casualties, assuming  2% fatality rate: 18640218.61 
##   Max reached at day : 57 ==>  2020-03-20 
## ================================================================================

Plotting and visualizing the model

plt.SIR.model(world.SIR.model,"World",interactiveFig=TRUE,fileName="world.SIR.model")